Présentation de R
- Langage de programmation pour la data
- Open Source
- Gratuit
- Multi-plateformes : Windows, Linux, Mac
- IDE : RStudio
- Plus de 10 000 packages
boxplot(Age ~ Gender, data = donnees,
main = "Boxplot de l'âge en fonction du Genre",
col = c("pink", "lightblue"))
tab <- table(donnees$Gender)
barplot(tab, main = "Effectifs par Genre",
col = c("pink", "lightblue"),
ylim = c(0, max(tab)+10))
tab <- table(donnees$Gender)
pie(tab, main = "Effectifs par Genre",
col = c("pink", "lightblue"))
Poids <- donnees$Weight
Taille <- donnees$Height
plot(Poids ~ Taille,
main = "Poids selon la Taille")
plot(Poids ~ Taille,
main = "Poids selon la Taille",
xlab = "Taille (en cm)",
ylab = "Poids (en kg)",
cex = 0.8,
pch = 4, col = "blue",
col.main = "purple")
plot(Poids ~ Taille,
main = "Poids selon la Taille",
xlab = "Taille (en cm)",
ylab = "Poids (en kg)",
cex = 0.8,
pch = 4, col = "blue",
col.main = "purple")
lines(Taille, 1.06*Taille-117.62, lty = 1, col = "red")
abline(h = mean(Poids, na.rm = TRUE),
lty = 3, lwd = 2, col = "darkgreen")
legend(x = 147, y = 164,
title = "Légende",
legend = c("Répondants", "Droite de régression",
"Valeur du Poids moyen"),
cex = 0.8,
pch = c(4, NA, NA),
lty = c(NA, 1, 3),
lwd = c(1, 1, 2),
col = c("blue", "red", "darkgreen"))
name=c(3,10,10,3,6,7,8,3,6,1,2,
2,6,10,2,3,3,10,4,5,9,10)
feature=paste("feature ", c(1,1,2,2,2,2,2,3,3,3,
3,3,3,3,4,4,4,4,5,5,5,5) , sep="")
dat <- data.frame(name, feature)
dat <- with(dat, table(name, feature))
library(circlize)
chordDiagram(as.data.frame(dat), transparency = 0.5)
library("igraph")
vertices <- data.frame("name" = unique(unlist(relations)))
g = graph.data.frame(relations, directed=F, vertices=vertices)
vertices$group = edge.betweenness.community(g)$membership
set.seed(125)
plot(g,
layout = layout.auto,
vertex.color = vertices$group,
vertex.label = NA,
vertex.size = 5,
edge.arrow.size = 0.8, main = "Network graph")
library(fmsb)
colors_border=c( rgb(0.2,0.5,0.5,0.9), rgb(0.8,0.2,0.5,0.9) ,
rgb(0.7,0.5,0.1,0.9) )
colors_in=c( rgb(0.2,0.5,0.5,0.4), rgb(0.8,0.2,0.5,0.4) ,
rgb(0.7,0.5,0.1,0.4) )
radarchart( data , axistype = 1 ,
pcol = colors_border , pfcol = colors_in ,
plwd = 4 , plty = 1,
cglcol ="grey", cglty = 1, axislabcol = "grey",
caxislabels = seq(0,20,5), cglwd = 0.8,
vlcex = 0.8 , title = "Radar Chart"
)
legend(x = 0.7, y = 1, legend = rownames(data[-c(1,2),]),
bty = "n", pch = 20 , col = colors_in ,
text.col = "grey", cex = 0.8, pt.cex = 3)
library(ggplot2)
ggplot(na.omit(donnees),
aes(x = Height, y = Weight,
color = Gender, shape = Gender)) +
geom_point(size = 2.5, alpha = 0.5) +
labs(x = "Taille", y = "Poids",
title = "Mon Graphique",
subtitle = "Taille et Poids par Genre",
color = "Genre", shape = "Genre")
library(ggplot2)
ggplot(data, aes(fill = condition, y = value, x = specie)) +
geom_bar( stat = "identity", position = "fill")
Dans R, on peut sélectionner des couleurs par : numéro (1, 2, 3, …), nom (“lightblue”, …), code hexadécimal (#E6E6FA), code RGB (rgb(230, 230, 250, max = 255)), …
library(cartography)
data(nuts2006)
nuts2.df$cagr <- 100 *
(((nuts2.df$pop2008/nuts2.df$pop1999)^(1/9)) - 1)
cols <- carto.pal(pal1 = "green.pal", n1 = 2,
pal2 = "red.pal", n2 = 4)
plot(nuts0.spdf, border = NA, col = NA, bg = "#A6CAE0")
plot(world.spdf, col = "#E3DEBF", border = NA, add = TRUE)
choroLayer(spdf = nuts2.spdf, df = nuts2.df, var = "cagr",
breaks = c(-2.43, -1, 0, 0.5, 1, 2, 3.1),
col = cols, border = "grey40", lwd = 0.5,
legend.pos = "right",
legend.title.txt = "taux de croissance\n
annuel moyen",
legend.values.rnd = 2, add = TRUE)
plot(nuts0.spdf, border = "grey20", lwd = 0.75, add = TRUE)
layoutLayer(title = "Growth rate in Europe",
author = "cartography",
sources = "Eurostat, 2008",
frame = TRUE, col = NA, scale = NULL,
coltitle = "black", south = TRUE)
library(rgdal)
library(rgeos)
library(ggplot2)
us <- readOGR("us_states_hexgrid.geojson", "OGRGeoJSON")
centers <- cbind.data.frame(
data.frame(gCentroid(us, byid=TRUE),
id=us@data$iso3166_2))
us_map <- fortify(us, region="iso3166_2")
ggplot(data=us_map, aes(map_id=id, x=long, y=lat)) +
geom_map(map=us_map, color="black", fill="white")
gg <- ggplot()
gg <- gg + geom_map(data=us_map, map=us_map,
aes(x=long, y=lat, map_id=id),
color="white", size=0.5)
gg <- gg + geom_map(data=us@data, map=us_map,
aes(fill=bees, map_id=iso3166_2))
gg <- gg + geom_map(data=us@data, map=us_map,
aes(map_id=iso3166_2),
fill="#ffffff", alpha=0, color="white",
show_guide=FALSE)
gg <- gg + geom_text(data=centers, aes(label=id, x=x, y=y),
color="white", size=4)
gg <- gg + scale_fill_distiller(palette="RdPu",
na.value="#7f7f7f")
gg <- gg + coord_map()
gg <- gg + labs(x=NULL, y=NULL)
gg <- gg + theme_bw()
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(panel.grid=element_blank())
gg <- gg + theme(axis.ticks=element_blank())
gg <- gg + theme(axis.text=element_blank())
gg
library(leaflet)
villes <- data.frame(
Ville = c("Paris", "Lille", "Nantes", "Marseille"),
Latitude = c(48.85661400000001, 50.62924999999999, 47.218371, 43.296482),
Longitude = c(2.3522219000000177, 3.057256000000052,
-1.553621000000021, 5.369779999999992),
Population = c(2249975, 227560, 284970, 850726))
couleurs <- colorNumeric("YlOrRd", villes$Population, n = 5)
m <- leaflet(villes) %>%
addTiles() %>%
addCircles(lng = ~Longitude, lat = ~Latitude, weight = 1,
radius = ~sqrt(Population) * 50,
popup = ~paste(Ville, ":", Population),
color = ~couleurs(Population), fillOpacity = 0.9) %>%
addLegend(pal = couleurs, values = ~Population, opacity = 0.9)
m
Shiny : package R permettant de générer une application web dynamique, présentant des résultats statistiques et graphiques réalisés avec R, le tout sans avoir besoin d’aucune connaissance en HTML, CSS ou JavaScript
